{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "S+P Week 2 Lesson 3.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "D1J15Vh_1Jih",
        "colab_type": "code",
        "cellView": "both",
        "colab": {}
      },
      "source": [
        "!pip install tf-nightly-2.0-preview\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BOjujz601HcS",
        "colab_type": "code",
        "outputId": "39cb29d2-af40-47b6-9171-280b77104bd8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "print(tf.__version__)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.0.0-dev20190628\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Zswl7jRtGzkk",
        "colab": {}
      },
      "source": [
        "def plot_series(time, series, format=\"-\", start=0, end=None):\n",
        "    plt.plot(time[start:end], series[start:end], format)\n",
        "    plt.xlabel(\"Time\")\n",
        "    plt.ylabel(\"Value\")\n",
        "    plt.grid(True)\n",
        "\n",
        "def trend(time, slope=0):\n",
        "    return slope * time\n",
        "\n",
        "def seasonal_pattern(season_time):\n",
        "    \"\"\"Just an arbitrary pattern, you can change it if you wish\"\"\"\n",
        "    return np.where(season_time < 0.4,\n",
        "                    np.cos(season_time * 2 * np.pi),\n",
        "                    1 / np.exp(3 * season_time))\n",
        "\n",
        "def seasonality(time, period, amplitude=1, phase=0):\n",
        "    \"\"\"Repeats the same pattern at each period\"\"\"\n",
        "    season_time = ((time + phase) % period) / period\n",
        "    return amplitude * seasonal_pattern(season_time)\n",
        "\n",
        "def noise(time, noise_level=1, seed=None):\n",
        "    rnd = np.random.RandomState(seed)\n",
        "    return rnd.randn(len(time)) * noise_level\n",
        "\n",
        "time = np.arange(4 * 365 + 1, dtype=\"float32\")\n",
        "baseline = 10\n",
        "series = trend(time, 0.1)  \n",
        "baseline = 10\n",
        "amplitude = 40\n",
        "slope = 0.05\n",
        "noise_level = 5\n",
        "\n",
        "# Create the series\n",
        "series = baseline + trend(time, slope) + seasonality(time, period=365, amplitude=amplitude)\n",
        "# Update with noise\n",
        "series += noise(time, noise_level, seed=42)\n",
        "\n",
        "split_time = 1000\n",
        "time_train = time[:split_time]\n",
        "x_train = series[:split_time]\n",
        "time_valid = time[split_time:]\n",
        "x_valid = series[split_time:]\n",
        "\n",
        "window_size = 20\n",
        "batch_size = 32\n",
        "shuffle_buffer_size = 1000"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4sTTIOCbyShY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def windowed_dataset(series, window_size, batch_size, shuffle_buffer):\n",
        "  dataset = tf.data.Dataset.from_tensor_slices(series)\n",
        "  dataset = dataset.window(window_size + 1, shift=1, drop_remainder=True)\n",
        "  dataset = dataset.flat_map(lambda window: window.batch(window_size + 1))\n",
        "  dataset = dataset.shuffle(shuffle_buffer).map(lambda window: (window[:-1], window[-1]))\n",
        "  dataset = dataset.batch(batch_size).prefetch(1)\n",
        "  return dataset"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TW-vT7eLYAdb",
        "colab_type": "code",
        "outputId": "31bd3def-0919-4c2c-9f77-0efe8e0e88b7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "dataset = windowed_dataset(x_train, window_size, batch_size, shuffle_buffer_size)\n",
        "\n",
        "\n",
        "model = tf.keras.models.Sequential([\n",
        "    tf.keras.layers.Dense(10, input_shape=[window_size], activation=\"relu\"), \n",
        "    tf.keras.layers.Dense(10, activation=\"relu\"), \n",
        "    tf.keras.layers.Dense(1)\n",
        "])\n",
        "\n",
        "model.compile(loss=\"mse\", optimizer=tf.keras.optimizers.SGD(lr=1e-6, momentum=0.9))\n",
        "model.fit(dataset,epochs=100,verbose=0)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f3c9752e780>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "efhco2rYyIFF",
        "colab_type": "code",
        "outputId": "0016930e-cc5c-4db4-8906-aa0214abff3b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        }
      },
      "source": [
        "forecast = []\n",
        "for time in range(len(series) - window_size):\n",
        "  forecast.append(model.predict(series[time:time + window_size][np.newaxis]))\n",
        "\n",
        "forecast = forecast[split_time-window_size:]\n",
        "results = np.array(forecast)[:, 0, 0]\n",
        "\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "\n",
        "plot_series(time_valid, x_valid)\n",
        "plot_series(time_valid, results)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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daTtyAL9QeT6jz3s31KbgyLY5B5+bkOVYsSHOlIJqeBBqDms+emiSz913kAf3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\nG+LsZJ2zsVKdlZ1ZMo7dCGvO4pzdvUs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diRLbUw75jBMJQ8MdO4b4yA9283tf2c63th+N\nxqOZHCQdYky2h4i/N315Ey7Vr8lUr8ZF7lUb+0g7VpOQbRoA7gd4gYqEdfyznSgl9znunK3K6Pet\n1yo12lZMHADgQLCW9eVdfD37F7ze+Q/SjsU6T392Fwb7eZa9V7833lT0vkU5Z7HQ4Ucz/8RVg/+G\n8usJ16pfxXLB9t/DoLWe/WqArlJDkDF5CJwMvHec1LVvxbIsdve/GIYew682wpV1L+DrDw1y4fhW\nveDIQ9F9Kylw3qo856zU7ZHSxx+GH7wPvv9uSHfAL32ccqZR/bzO0m7hCqZZZZe0G2ZEmeFVf83k\nf/4ye9V6RlQv11Tu08srE9A9QLtxQnFmWdZay7I+bVnW98L/X2ZZ1s2Lv2uCMDemWjMSZ2eJc9aq\n35HjzLzqM9VIRRpz5jZ6B8mrUuScAfpq1ElRzKyhqz4Sva/C/Hhgvz4xmIT15rBmB/r/U+W6ds5C\nx8zcHlUrsUNn04izzbkKVijy8plUNJezUvfZN1JkuFCNhMPFD78f/u31/Fbqdl5w7HO8pH5PJFAA\nKOl8oPNtLbzXWLrfU19nhlzaTrhx0ZDuuh+N+irVPS6xdMXbxpR+7NqeXGQ0XDrQyCMzmAq8Ujgj\ncqRYI5uyyaRsXmI9yjX2Hs6p6/zG9b1GnM3MOzs4rsXNNefqZslXbujFsa0ZeWeJsGba4WbndtZZ\no6zrzSWqSwGOTOoE9PNWdRIofQuN30vda4wtahXWjJyz0AkzYc14893V3Vm6c2lGzSQI26JU8yNH\nDmKtSnKNxquG5jFNGcemK6ufd7WjX083FfKZ8EOYPIjC4gm1mZ5AC6DLrINkbOh0x9kZbMKxFOtD\nEZPzC+E+NHLbOqyZveZW+mPR8wL0qVgu2OAD3Jd+LvvVAB2FA43lEwehdxPYDpZl0ZlJsTd/JagA\ndehnAHRR5nWFL/Ker23jPzkP6MdVJ6NNvNa5j7epb/JPb7qad77sQjbbuvkvb7kN3vkwbLqe7Vv+\ngC97NwAwYMSZVeCcaT0EnY3XJ19MrpdU33rA4v5gS/K+pSjOgM8CdwDrw/8/DfzuYu2QIMwX0+fM\nPttyzmLl7H/xi5fzv1+zpaVgy4ZXvCWlT37H05s4xz9MZ7M4C6nlB3iF/RDV7/zxIu358sTMh9w7\nEoawmsKaGcsnhcdkRefs9Ngm50zfHlUNB+AJpcXZW9Q34e+3gFtNdLwvuz6//YWH+cB3d0ZOTcbV\nbsavcAcA1wcPNx7j1aDeyHsCWG3pE2x/Pk0u5STyy5rDmhlHDwQ3Yc0N4UnQsa3oe3jpQOO7ZL6H\nZjxVpe7z+1/bzufvOxiJkfPQTs5KX+cVre7OkrKtxAQFw84hve+mR9w5K/Ks6c5GvbEMVS+Iusiv\nLTzBn6W/yA1T/86mFfnE2CzQ+XtrurO85Xnn6v1pcs7qXiMRvtayICDpnE2G+W+TserpbNj6whQB\n9HdmuP3xYzz3b37Y2OdQFJn3rOJ60WObxVk6LAi43nqKjZYWKral6DLzcScO4ncOMBzrW3ixfZgV\ndhFbefwwuDqxvbyvvzOOX43yAztrYcHIdb/JF9WNAKwORuiJtfDojYuzTBe3dbyOJ4PNZKcPwKH7\n4alvaxev/9xotY6Mw+70pWA5cEi7VS+1H+V/pW/lF517uc7eibKSUuQ3U9/jxuFPsrbT4fdfeQm2\nCZWe8/woQjJ83i9yq/9iANaghd0Kq8CG43dDxwrY1CTOaHzG9weX6QWrQ5HWZsUAMD9xtkop9dX/\nz955h8lRntn+91XoPNM9WZqgLCQkQBEQmCDANgZMcGad1rvYZu1dex3WXq+5u/a1vd6119lrHNYR\nruNiHLExNiAQOYNQQHkUJ+ee6VT13T++qurqiS1pBkmozvPokdRdXV1dXV116pz3PS+oUBUpZQEY\na54HCPAioxilcWrVnPmJ2NJZFbzzwgXjLldUzhQ52xJdQ5QM9fkDpbamg9azPshO2Uj4uVuVNyUl\ndGybgU/w0oIbW+GRs1HKGUCMLAMjeSKm5pGyCl/NmYuddhMZadJkHYThbujdQyykOxdkyUjOojud\no20g49mRdlzFF1ShLrjreJ647qalFjv3XNQLdSGLhwxihs1PzM9Q2KSKrF0CMpxTtmbI0Di71qJO\nDCATs4jYad6o34t++GnvOPQrZ26Mi6ucDecKPLlXqW1XnaUugLNz6kJbU+iAh/4b/f7Pc3qs3xsp\n5cdDO7uoTYQ4f6HaR42pKBFTHzNWSdWcqeN91sG7AFg4/CwtVTHaBjIlluPh/gyzk1Fev7aZVy5r\nYP2SenjiB8waUlEmOcdu9O8P/79dW3MoWyjpbnVJ2qeuXc5fnTOnJJfMVcy6hrKeMu3NLnWWG8m5\nSprp5dy5cSphXePcff/DL8Kf5tr+W731xmzHZuxrhaq59MpiZMQScYBah7Q8b8+nzUfcYtYQC4wu\nngu/k0S7UprCaSdSYtm13G5eBUCT6PK2DyDl2prLXwuv+g+yZpIfW5dhhyrgB1fCz98Kh5+B1Jzi\ne4V0+qwwzF6B5pAzV719j/5bdCRi0cudpYsOgC4tz66ld68iUGbUt16DYccVMIX6Duroo+bQBjjt\n8nGjldxz4m3WRfy06Sa42BlZd5IqZ2khRA1O/LgQYh3QP/lLAgSYeXjKmSyA0GCcuquXIvzdmrHQ\nxITUvUsckuoEtjVyFgCmzEN4rBUVXnQBv7HORy8MK4vh0W/DzefCgSemc/NfcnBT43e5VlthLMmI\nksWWqq4nIRxbSri2plKFpNBpp4oefN9N9y7m0cZ9oQ9wqfY0mbxFOlugJ53zCIeZK56O95oLqRQj\nzMs4mWlpJ/NKM8ihlOU11c64nFSEeQOPc76+Bf2ZW5BSFm1NRzkzdQEdal3uBfTz5v/w6dq7veNw\ncUOFZ3E2xgVfNL+J1bMXUDdQBdvmtaub+MabV4OUpNKq6aR6/1/grptgw2f5lvVJtOHOUftV8uCu\nbs5bWMuc6hiGBq9r+wpr2DJmnFXWtTWlpHqfUhAb09uYXymREg72FlW5Q/0jNKYiVEZMvvP2tSyq\ntOD3H+TiJ/+BV2mPQfvWcQefu2SqOq7243DWKskadIe3v+XcuURMnYqwfyZp8ZgYchQ3v3L2X8a3\nqD34F0DNjPSIoBMwm8oe4Izt31DfW7bYIRstOKpobyt69VwGtOJNV6UYZmFeNSj0iBSb7OJNXEoM\nsTasMsoSfdswNdDcvK/KJvpMRfibRJdHwpNRk+Up5/O++kuw+u2YukY/CTLr/lEV1LvuRaqonFVG\nTEVcG1ehdambvdmaOmYXaoeRoQQsu1YtHK8lg2/SS9d27/NRNQ8/YiG9JEQb4BL9GYxcvyJn48Ct\nWcwS4vnaK2DeBRCqgFlnjbv88UQ5V7MPAb8FFgohHgRuAd43+UsCBJh5uHegmrROGdUMShsCYuPE\narjwyBlR+mWcvYZPYRvH1pxVGeGwq+L0H4Btv1f/9rXmBxgL9yZhV8dY5cyWzigtoR4LGxoVbpQG\nrq3p7PPKJix0+qSfnO1kcWE7upAsEQcYyhYYyVuKnDnKmZEt1ur8wrwOgFkZVfjNsEPOLvwnflH1\nLnplgkua4bGbLmPprEoWtf0RAHP/Q+QzxToutyHA1DWPnLH45d7zZyWHvYt2c1XUG/G0InSA1+kb\nObPvHm/ZrqFccXD7YBsi66gvh1VkA6/5NjWyl1e33VyyX3d2DNE5mOVlC2uoSYTZ8I4mFu79Kdfl\n7vCI0s8f38ff3fpksSGgeyehgVbutM5Gw+KyJ97D6/X72O+QMyklh/syXp0bAPseBSSRbDffCn2F\n6j/eOL5yZpUqZ+lcoaSrsnc4T0jXPLXLrzh99fqVnNagVC03BsQldrUxnTcY99PSrixPN2LE/++q\n9F7vsahd7AA1Hv4a/PKdMHAQUTWPfEjV5m22FTlannlKLVjRwDcLV/OJ/F8DKnx6kaFs5fBwO+83\nboetv1XLVjaimTH6tRRNoot1C2r4h0sWce8/rccY6Vb2ZES9j9slrl3wQfjILjjdieaJJL1tbK6K\nKmu5cjZapo8wOWbrxWOWuS+DaufcFKtlQE8Vn/PI2d4x5Cwa0hmWkZLHVmhOt3ljqY3rQgjhnRdN\nXYNEPXysteTYPlEwJTmTUj4FXAycD9wILJdSPjf5qwIEmHl4tiaFU6beDNQF3r0AlBR+j0JIV8/d\nba/m1+JSOrQ6LNc2GMfWrE2EOIxT/9R/UBE0ABlUMUwGt8R7KFtQF14nhHaP3cCD9nIA1mjbuUp7\nRClnjp0ZdxS0LpLkMBCpOYR0jR6fNUXPLubk1QWnWXTyyr6fsVLspHc459lpeqYX5l/MLZU38tv8\n2dhSkCj0wFO3wE6nxumM17Ft7pvplElShR7qKyKQH6Gp7R522bMRVhZr9/3e22ZyFlnH1qRji8rF\nm73Se14MHiYRMTA0QW0i7I0Qmh9WxGtOtjQvL+k8T6djk8ccQmrG4Mw3sDF2GWvSG0sGTz+2V90U\nnDc/CY9/l+a2PwNwZmETWaeW6+Fd3WzY3kGm4ChnzsX8R9YrsYRBReeT3KD/wctK6x/JM5K3mJ3y\nkbPWB0EzeeyiW/hZYT3h3u2kCsoO3nyon1d++T56fXM3izVnY5Uzf2eja2tqAq5Z0chHLl/qLKde\n4zZ5zI+r/8eHVYjsgtribMpq570qRtRv8Vl7VAnDll87+WESquYhoyqr6w5bzbJcMqzIWaK6iWc4\njR9Zl5PXY6REmnlC1ZjVZFp5n3YbvPAH9b2Yyjpu1+ppEl2kYib/dPkSFeo73K2WcaRS0yE6YVOH\naAqu+DycfjUsLeYnzqmJsb93GDuhrMN60UuDKJIzseBi1UAAEKthSE9hIxQB7Nqhfk8DBydQzkrJ\nmY4Neri4vnEQdgilNyHgBJ0sU0635tuBNwNrgNXAXzmPHTWEEB8UQmwWQjwvhPipECIihJgvhHhU\nCLFTCPFzIURo6jUFOJVhnaLKmeqAcgM3p7Y1b7Mu5ubQO8jYOm1u8fk4tqaha+TiTmHswAF1QgTI\nDo5ZNkARrnIGsLNzyIvSeHv+Y3zHUhepf9B/zafMHxD2kTPNoXU5DNr02VC3hETEoNe1NROzoHsX\njVmlgi3R9vPe/C1cr99D3pJ0Oxd3faQHquaxofoNHBqy6aZCkYs7PgwP/7daV6yGa1Y0YSRnY/a8\nAPd+FvY9jGEN84XCG7GMGOHf/h1/rStLcCRvkXcaAmjfAvXLoNLtCQMGD5MM68xORdA14ZGvFkPZ\nVcspWm9QjNvwVLiFTqZVw3LQdB6pfBVhsrDl12QLFoOZPDs7hoiaOnN6Hlaf5d7PApC0+5mV3QW2\nqr/L5G0GMwWVc+YEoJ6z7mLaXnMbcvHltIguDnQPMZDJc/8OpSQ2Jn0X9daHoGk16cbzuNV6BQCr\nLKU/PLVPZZXt7hoam3OWLZSQs4FMoaR5ozKilgs5kwvcfdDnqG1dTtxIS0SR9Epn/NKi+iI5d1+T\nGN6HbSbYYhftwjFIzaW7cgkPWMvZGFnPXruBCqsPQhX809Wr+c/XKusuY1SSFGmaUTbmktzzY1YV\nNXUOyVqaRBeGv1xkuBvixQYWUxNETb04+zdRD2/6f1BRHEw/tzqujldNEfIGeqmjjw3WCn5vvALO\neL2qJ9MMiNcQqZ1LX3whzD4LnvkxfONcXPLpR8w0yBDy1GkPNQsnJVzueTE0TjbkiYRytu5s358L\ngU8C1xztGwohmoD3A2ullGcAOnA98Dngy1LKRUAvEMR1BJgUtpdzVjhlMs5c+NPCJ4J3Z4gKKrVs\nyT7pzL4bx9YECFU2UEBXNR5uXldAziaFj5ux5VA/X7tLJafnpMmwVPUzjaKLBCNEdEmMYqehLQwk\nGp9M/Qe84v9SETE4LGuwQklYeCl076RhRJGzVUKFbroD09VgaAmZXohVEzE1bAldMkVqaEfx+xMa\nRKs4Z341C+YtgO6dcN/n4N7/AOAxeynbL/s+MlzJlbqKOnBtzbCOSm5vWK4iV678gppBaBf4wPnV\nfO516oLvEpGkozjN1Tr4oPG/LBeKpHnkrPUhVY/UtEb9v0FlUbVXnMF+0Qjb7uBLd23n2m88yJ6u\nNPNr44i9G50dbcMiRZ6+3PP38Lt/9Loae13Vqmc3RKv44DXn0nTWJYilV5EQI6Tbd/Olu7bz/p+q\nEVmecvbsz+DQUzD3fExdY4ucSyGcYq2tyJlbK9Y/kifn1Ll5tmbW8lQwF1FfVpirnIWdcFy3fsxT\nzpyO1sawY3PnOtGxSsiZq9LF0/uwUvNoRxX1p0cpRmrhuYQr6nhr/iZEag4PuR2JFQ2cPrvSmzgx\nrFeSZIiGgiJnVf4OTKeBJGxqHLBraBTdmBqqjvK+zyvlM1ZsYDF1bdJzEKgOW4CDBWV1zhK91Mhe\n9shZ3Fzxj4rI6Yb6buecT+NffZ3qd/4SKpvUCnodol+3tGS9igiLsfuiZtGk21Nia57AKMfWfJ/v\nz7tQ6lliqtdNAQOICiEMIAYcBi4FbnOe/xFw3TG+R4CXONyLojjFlDNQdWchXZv0BOMfFqyGOkta\n7Xr1gK8mxI+6ZJwuUQN7ihYXuaFxlw2g4M+F+92zh+nqd8isHkKa6sIUEhZhUSBJ6b6UutP5F6qF\ncAWJsME3Ctcy/LY/Qu1iGGonketkRIa8KQ9ztA4WiEPQtZ2UlkHYBYhWezlfnTJJrGdr8U2i1cVm\nmWixY48Dj5FNNNNNks6atWTrV1Dr9HqpnDNbJa/n01DvRA6c8y5wGgNOiw2piIv2LXyk/zN8zfw6\nyUxx9uc/Gr/iXcYdACxvvVXVRrU+qOYcphzbadaZAMTDJi+IudC1g12daXZ3pnmytZcFdXF1LM5e\nqcjq+o+xO+KQjtaH6HHUQ+k0W9Czu1i/5Fs/7Zt4el+v93BjMgL7H4Nf3QjNZ8O69xIyNCQaffXn\nsEIqW9b9avuG817NWTSkEzE1hnOlyhmUBrmmzDwC2yMD7gSJe1/o4LIvbmBPV5qKiOHlkulYzKKH\nRfUJqhhghdhJTVSyXnuG6GArono+bVLZlt2iymvwoG6pl3LvDnufVRnhIdsJYU0oFcsljkNagnrR\nR1WhdEQSq94Kr/0f73NsK8wiKnJUZ/bBcz+De/9dEXu/cmZopXMvx8FcJ0h2d0bdEM4V7SRI0ylT\npRFAb/4ZrPs71TlZNQ9WvllZpB/eDu95CBpXlqzXJYUeOYs757baxZNuj6ecGSc2OTuaK1oamH+0\nbyilPCiE+AKwDxgB7gKeBPqcmA6AA0DT0b5HgFMDll85O4VqzkCRs+gUd6zhEuVMI5Oz2CedE9g4\ntibAvNo4+3dVM8st1oZAOZsC7gW8KRXl8dYeztLUacwMhTHNBPiaN6sKozoSHXLmWiwVEYOsWUmi\neRmYEu7+vwDca6/kSiess5FuvmF+FdkV5XnjH9SKYjXeRbKDKoQ/zsN3MfUQrYaRHkbqV0GXk2sW\nqaFGqJqvTN4iX5DMtR1r269aVDj25uZfQd9+OPQUq9MbWa1DoS1Oh9lEfV697iyxmxgZ5m3+72Le\n2vwLoWmt+rNI2ZuxsM5uazb0PkafrhoTBjMFlqUKsH0TXPJxL/bgv+d/kyu338TLOUSPLw8s7JKz\nlnOL21p/OjYadentbB5cyaVL61neWKkiP/50M4ST8JbbIJwg1KvqoEYiDVTxYMnuUsqZ7X1X8ZDh\nNARMQM5smzc/ci1t+iv5o/EGoFh3d9fmdmeWaIG6RJhwvqhcNYsu6irCfMb8PpdpT/NE3xAfDX0e\nBoAzr+W6i5bCw99lSCQYFllCdg+85ltKZdJ0b9j7rGSEP7jKmUPO3BKIARKcJZ5GQ7LVbuF0bT/9\nVJC89hvFw8PU2Vg4HXRo6HoUnv2579gpEvy3rZvLpUvrmAyzkxEMTbBj0MDWw5xpKSWsg1RJ08QY\nzL9I/YESm9S/jQBpGVEJHNULIN2hRjtNAve3Zo4T3H0iYUpyJoT4HcWaVw1YBvziaN9QCFEFXIsi\neH3A/wKvOoLXvxt4N0BDQwMbNmw42k0pG0NDQy/K+wQ4MuzscxK6e7pI5Qo8Ouo7eil/b/nhEXQp\nJ/18e1uLF47s8BD9WckO2QzAI5v3ktk9NpHdGCjQa8dBh0y4hlBugEO7trFTG/s+BVuStyFqTO9J\n7mT73vbsVQQhpWc5KCGE2u+WFMRMrYScDe55uuS1OUvtu4G+XjZs2EA+nSFpSu677z4A9At+wr49\n27h7z7BHzjQhOV3sJ2OZ1EqlBm3adZDuTkXCuilVRftyBs84+9M0XkbNkhCaneO0Hd9mV0695qln\nn6duMMsqkea9+q+56uGnuV18iqgztunhLfvIOsdLONPFeQAPfJlsqIa+1BlUESJEDqOQ5mB4OX+V\n/QBvjjzCDdovebt+F0ZukIIex7DSPHxYJ9u7FRb/Kzy7F9hLV1uOgXw9hCzo2gmo4vHaA/cAkqd7\n4/Q7n6G7M0tXIUZ+oKNk9FT7vh3IvgO0Js9jr+/4WRFuYoW1m4ItWRruZ00ozSN/eo51m3/DgeZr\n2PWwioppHVDnkwO9Gc5lGIGNdMylZ7fu8Ej47p9/nJVWA9U7HyGm54ka55K31fiqbHqADRs2EMp2\nc362i5frT/G7GH59VgAAIABJREFU3NXe8RzW8UJfOwezVBl5elqLKmez6OTJv9zO5doTGMJm2Z4f\nec9t7xhBT6rvYEhGGRJRkgg2bu3A1vuBLXQdVMfecNchukhyf+xyauRCOjdsQDo3s+0Zw1NhH7aX\nc7q2nzatnqd9+6y3K0urbOCgrGHe5q9DoZeumnOp7X6U7t3Pssm3bBWwYcNOJkNNBJ58oZW0nuIM\nzSFnMkW2r/uYfushraicteVizAKeah1goG/ideYyykZu3bOLDda+o37vmUY5ytkXfP8uAK1SygMT\nLVwGXg7skVJ2AgghbgdeBqSEEIajnjUDB8d7sZTyO8B3ANauXSvXr19/DJtSHjZs2MCL8T4BjgyJ\nvT3wyMNUJyuIphNjvqOX8vd226GnsDqGWL/+ogmXaXtsH2zdBEBddRV9HUP82V7Dr8+/neteedm4\nr1ncN8LdzyvrJHL9D+H2G2muT9I8zn780l0vcMemw9z94bHPHQtOtu/tqfx22LmD1Ytb2NzdSlio\ni28ymeK7N14Mny8uu6whhFOHDYARTcAQNNTXsn79WhqWDNA/kmfdgmJdz53Pr2H/7t8AsMNuYrGm\nTo0Rkeec0F6w4MxzLmbR1iR/at3FSKhWxYQbEWg5l1Td0lH781oY6oDbttC4/kbYuZsFi5dQ03sG\nHIZX6Y8zP7+HuZVpWvQcZOG8y64B07GPrAI8okqCw7luGvQ+DiTPQOvbS6PoIVQ3n139TbRXrYGe\nX/IB45cUGs7CeMUnYcefOe9VbxizD7eyi7t2q2aBBrsdl5xdMMeEQ7DqFddDXO2TDQOb6e1KYhTS\n6FhYKAXl7OYYYr/NvNWXMm9F8fMWMtfwsoe/RS39XP/K69S8xl/9HegmLa//DC2OxbqzYxAeup+K\n2QvQuiQVDDNAggZ6+NfOz/HLJV/kT62wdMc3+R7gOtTfj93OcE4pYbPra1m//mxlmT4MK8Uu6ivC\nav/bFrMfuou9A0VCubCpnnmRKPnDOrqwaRadXBB+AenoIdWFdka0BFF7iNMufiNUzYcnPsBp8+cS\nGQ4hMjoXXVbM9BrZdJhbtjzFeSuXcX/bNg6u/wIXnVMMhI3ecydDRhUUIBtt4O7+Nfwtd9JhzC45\nRu7tf56NB1t5xF7G6wobYdaZ1P7tr+FXN1Jz/vtYP076/mRYtucxWnuGEVVzaG5/HIB2WcXqOY2s\nX3/0GWOJjX9mOK+Oy1mXfwD2rmX1xTdMWoNcs+VBWgf6WL50Cet9++ZEQzk1Z/f5/jx4jMQMlJ25\nTggRE6rF4zJgC3Av8Hpnmb8GfnOM7xPgJQ73TlbDOuVszX9+1VK+cv3KSZcJjao5U0OoBSOpiWX/\nxmSEH0beypfnfVNZCuGKCW3N1p5hdnWmx6S1n2qQUqIJigO0yZOVBvGIiR4uLc9NZJWtmZeO/TXK\n1jx9dmUJMQNVW7NP1tNDJT+zLil57lzhdNrFqj2bJxtxbMzKRnjbr+GKz43d6EQ9vOP3hGpVfVam\nYJEJqfc93ZmjuTz3HFWyT1l/pq/oevSFr20TmVgTm525oFpS2Z4D1SpGJCLyiPUfUxbmFf85dltQ\nwcq7peoUnlU44IXa1uYOqeaVWLW3bNjQ6LbjCCRJitlstTnnft5fcwYYa/4aU1i8OfIgLdVRaNsE\nz/4U1r2nWPtGMXomLdR3lhRq3Wdpu6nJ7md231O06GPz1yNmse7Ki9LoU/swLPIsl6qRg7v+lZ8U\nPkjRiILaRJhQro9uKukR1czX2hG77uZQchU7hSIO0Vf+H/joHmheq/aDESFVXUdk1Ztgzd+UbEtN\nQjWgVEZNNv7zJbxpbWmkRCykk3PGRA3MuxyRUkp6t16akF8dV+u5x1qFFLpqBAnF4E23jjsWaSqs\nX1LP7s40vZr6HnOEOChrS0bRHQ2ipq5sTVDf+6U3TdkcdrLUnE24dUKIQSHEwDh/BoUQAxO9bipI\nKR9FFf4/BWxytuE7wD8DHxJC7ARqQN2cBAgwEbwJAXbhlGsIaKmOlcw0HA+hUTVnw04u1mRNBEII\nFs1p5jedTqRGOAHZ8RsC3EHPHYNj7dFTCbaUaEIwv1YVPleHIYepwoJ1wyNgANGsKsL2pgAY6iJo\nTFL/EgvpZAnx1tSP+Z51BRlp0mrXk5M6a5yuQn9DQCHm1BVWNqlGADHxut3XjOQsRkx14XRH4ZxR\n2ETS6oXE5DVFSBujeg6b5Ty1zipVLhxP1bPDbuJJuRR96ZWTriIWMugnQT5cxQLRxjsvmM9nrjuD\n0OA+NafR9xnChkanpYhwlSjeOFRl1fzP0eSMuiUM1q/lXZWPqsiH7SouhPPfX7KY6djzQw45WyDa\neKN+rzdUO5XZx3xdfX+bQ2fSrSkSHNOlN8c2Ymjw/O3QU4wSWWk9r4Kcn/g+jfZh5op2NGxerj1J\nbTyEme2jVybYop3G+dpmOPwczSsuZdEa1ZnKnPOK5FQIuO6bcM6NcO674cIPlXyGVXNSfOTyJVyw\nqJawoaNppd99NKTTVlD7bnDxtcQbFnK7dQHPRUsJlzuG6w77XJ56/UMwZx3HglcuVzVjbf0qRuaW\nihsYIlYSqH00iIV0b4TTRE1OoxFyumdP9G7NCfeMlHL8iuFpgJTyE8AnRj28GzhySh7glIXbJSdk\n/pSL0igH/hyfsKl73a1T3TGunJPiri3t9KZzVIUSE3ZrDjrkrH0gQ3NVbHo2+iSELUETgnPm13D9\n2S0s6w6TO2iQcKc3mDEv1iI60gZAt0yqIE6HuE12oXAbP+qTEba0DfKQvZxtcg7rtWdZZrcCAqIp\nIqb6nkSiHjqBZPOU2+6Ss+GcRdrfyQmsLGwibTV7BeUl+LsHITsAP7gCgLkLl7LOqoJtkGiYD2Sp\nipm8vfBxkpVJ7pyEIALevuqPzWHRyEEWVrSybuAhRXLqSyMUwqbuzZBM+bpfkyP7HZWtVHkEqDjz\nKtVcke6CQ0+ruIV46XLu72UARV7+Rr+T9fqz/M5SxKRqZB/ztRTY8L3aj3DawKP83dA3qNcHyaNS\n7RcVdsBt74JIEjuc4omRBl4x8kd4sAYKqtZptdjB6vBBvswXuSe7CCPXR5+s4CHO5CLxsBLWWs5V\nRfDSKnacujjjtRPuR1PX+PtLJo6SiJo638pezt1iOV9tXseizgN8aOt7eVWo1Hmor3BHKInxv/8j\nxOxklBXNST5+8Gou0Zp50LgSGCyZP3o0iIV0slpUbWeovBCJ0OgQ2hMUZW+dEKJeCDHH/TOTGxUg\nQDnwbM1TMEqjHIxWzrzHp+hSWtmiLjTPHOhTtubAIfjCkmLavINBp7C5rX/soO9TCbaU6hoWNvjP\n151FbcRRzlzLJlRMfI8OKbvrsBOJIBzlbDJyNr82zkWn1XHhYqVg/T3/woE1H6U1uVbZ+cuvA033\nbE0j6aielVM3vOuaoDYRon0gQ9ooWocvhM9kFl3Mzu6G+DjK2awzYO75nlohUnPINKyB639K9bJL\nuHZlIy9bVMtQqA49lhr7+lFwOwl3x1awWuzgzOc+Cw99Dbp3qDorH8KGRo+jHVSJIVxxqGJ4H1TP\nH18pnHeB+rv1QTj4JDSuHrOI+3vpl+pGY7GmKnjO01QtXG12P3NEO2gG6fAsuqT67HVav0dyGyyV\nuk+mH1ItfDH/RqqtLnjwK7D01WS0OGu07VydVMpay/AWjEwPvSS4z/KRsOa1MHsFXP3VaU2wj4V0\ncphslvMJGxrzatSx2ZctLU2oryzOt5yusNYVLSl2yGa+Y11Ns5N9Nt83DeFoEA3p7NeboW5J2bOV\nwy+VEFohxDVCiB3AHuA+YC/wxxnergABpoRVYmueWjVn5cANv4RSojaVnH9Wcwoh4Jl9Djnr3w9D\nbXD42ZLlBjOqM+xw/8h4qzl1IMHvHsV1i5w0ipaNWVQVzaGDjGhxBp2BzcJ0ydlktqbBLX97DqfP\nVoQkHtb57GvO5IoPfQ9uaoM3/BAoqmDxZA28+iuw5q/L2vymVJSDfSMMiyhZqX5Hj4WUiRGzBiZX\nTlwLMdmiwm6XXomu63z1+lWsmlNFLKQXA2gngTtE/f7QhZjCIt7lmxA4Khk+bGj0ObZwrTboWXCx\noX1jLU0XjavU97DpNhg8XAzB9cH9XfRKRRiahApkrXWqeGryh5gjD0NqDqZp0m6rsoI6MeBd8Gus\nYlSKSLXwhFjG/cmrYeVb4PXf51DFGazRdrBaqDFTjekt6Jle+mSCnfla9jNLxZb4auymE/74nap4\nyFO8u0ZkyXJ1FUVyNpnlfiRwQ3gB3n3RAv7fDedy2enHpsrFQga/DF0H732k7Ne8ZEJogU8D64Dt\nUsr5qAL+8vdEgAAzBLfmTIXQnpjz0Y4n3JOQJtSYldGPT4RE2OC0+gqe2d9XmoeW7oLDz8Ggsubc\nSID2gaDmTPOpNRHNJodZDNgMxbCcU62QNkNGiqxUdqYwVL1MORcKV13ySJ8QJXZ+NOQQhHgI1v4N\npMozOJqqFDnLWpIuFOF4SD+7uMBkNWdV8wExoYVaFQupOZ5TwP1MD6eb2GU7yp8zi3EsOSvamrND\nIySjJgYFIkMHJyZnuqkK2d3h3uOQM1dJ6bXGWvQFqWHKPCutTVC9gLCh026pfVVDUTnzB7uK1Fwq\nIga/avwnuO5mMMJkG89libafZP8WJIJ45zNomV56qCBvSf4j9A/w6i9Pub+OFq662pSKkggbrJqT\n4uLT6njL6aXTEmsTRXI2XSQmGSu+R9jQuWDxOPl7R4hF9Qnm1yUmrascjZeSrZmXUnYDmhBCk1Le\nC6yd4e0KEGBKuLk9wi6ok2+AErh387omMPTylTNQ1uazB/qQ/jqOdCf85I1w3+eQUno1Z20Dp7qt\nSQk5M8mRqkxw9QonrNWMMyAqvVFOaaOKrJPurplT25ou3AvrRPNUI45S6gaRloumVJRDfSNkCzbd\nspIREWWHNZsu6TScTKacnfFaWP12r7FhNG5+y2r+5Yql4z7nh2sB7+sd4Wb7tchzboRVb1FPVo+y\nNU2NNBHyGDSYw1RETBpFN0IWJiZnAKvepj5L1fyxdVyApglMXdBbCFGQpd/Hc1Ktt1IOQtV8wqZG\nm0POqmWf16VZmesA07HqquZy0eI6Vs0p2rqnX/NhtGQLQtqIJVfCcDdCWrRLVe+3xXTs4hmCe+ws\nblC/64ip86O/PYf5ydKbW7PkfDE9ylnSp5zp2vSs8+NXns6tN5w79YI+FJWzkzyEFugTQiSAjcCP\nhRAd4OtfDhDgOMGZpqJOykHN2RiEfOTMP/OvnDvGM5oq+fkT+xmSETztbOCgsoSGu8nkbW/wfHt/\noJz5b9xFIUddsoK6Wc6eC1fQr1cRtyUxsgyb1eScU6/Qw1y9opFz509tY7nfoddoMApnNid5zaom\nziljXX40pqJk8jbt/RkOyloqTZN0zmKb3cIF+ubiWJzxcPrV6s8EWFBXXpG2O4qnczDLA5WXIq58\nuepwrJo/bs0ZCHqpYB7tvCH7K2YZaibopOTszNerP5MgpGuM5C36iVNDsRP0Xmsli7XDWJpJ6rTL\nCW/T6LVMMoRIyX7myIP8u3ELycxBmHMunP1OmHcBX1s3qoMwmoI3/hAe+IrqFn3hDqyaJdx2UOUV\nGjNstbm//dMayu/3my7lzG9rGtNEzo4GJ0uUxoRXNCHEN4CfotL8R4APAG8BksCnXpStC3BKIW+p\nC/5Us9pc2EHN2aRw5XtdCBbWx8c8PhncmpMhokVy1uZkamUGvHozgLZT3NZ0+gGKsHIl8Rlc8nFu\n6Xuct7Z/gTrRz7BZTRbnzsII8/XXryrrfSKObTmRclYRMfnymybPvhsPTc4Q8D1daX6RfwfXLKmh\nf3ueF+QcLmCzykSbYfgjFWqcjC1i1bD6bWOWdWspe+wE67IPsC77AN1aBVakGt2dAXqUMA2NdNai\nX8apEYMMyQgJkeEF2cJ59ndZ1VLDrYvPJbxzG9mCpMdIkbR7eXXPj1ht3AODwKK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i/rnvww3wl9ySNnIUOD4R618GP/Axu/wGnWDhZ0bQDgE795nqf29XFaQwXVhU7mPP0FyKfhjNdO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\nOvL/AQ40/R/YnEF7AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-kT6j186YO6K",
        "colab_type": "code",
        "outputId": "f0f07197-2cbe-43f5-bf6e-49c1f5f9bcbf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "tf.keras.metrics.mean_absolute_error(x_valid, results).numpy()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5.105463"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tnCe_nBKu7RB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dataset = windowed_dataset(x_train, window_size, batch_size, shuffle_buffer_size)\n",
        "\n",
        "\n",
        "model = tf.keras.models.Sequential([\n",
        "    tf.keras.layers.Dense(10, input_shape=[window_size], activation=\"relu\"), \n",
        "    tf.keras.layers.Dense(10, activation=\"relu\"), \n",
        "    tf.keras.layers.Dense(1)\n",
        "])\n",
        "\n",
        "lr_schedule = tf.keras.callbacks.LearningRateScheduler(\n",
        "    lambda epoch: 1e-8 * 10**(epoch / 20))\n",
        "optimizer = tf.keras.optimizers.SGD(lr=1e-8, momentum=0.9)\n",
        "model.compile(loss=\"mse\", optimizer=optimizer)\n",
        "history = model.fit(dataset, epochs=100, callbacks=[lr_schedule], verbose=0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2ZaNsM2IgCd_",
        "colab_type": "code",
        "outputId": "fdba8582-38ca-495f-95d6-7e76d8296074",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        }
      },
      "source": [
        "lrs = 1e-8 * (10 ** (np.arange(100) / 20))\n",
        "plt.semilogx(lrs, history.history[\"loss\"])\n",
        "plt.axis([1e-8, 1e-3, 0, 300])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1e-08, 0.001, 0, 300]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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fx97D3Xz9kZ280nKEueX5vG9FNauXVzF7Wj6DQ46dzZ1s2tPG8toSzqssQvyZ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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QDwW0Q7ovYK1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "window_size = 30\n",
        "dataset = windowed_dataset(x_train, window_size, batch_size, shuffle_buffer_size)\n",
        "\n",
        "model = tf.keras.models.Sequential([\n",
        "  tf.keras.layers.Dense(10, activation=\"relu\", input_shape=[window_size]),\n",
        "  tf.keras.layers.Dense(10, activation=\"relu\"),\n",
        "  tf.keras.layers.Dense(1)\n",
        "])\n",
        "\n",
        "optimizer = tf.keras.optimizers.SGD(lr=8e-6, momentum=0.9)\n",
        "model.compile(loss=\"mse\", optimizer=optimizer)\n",
        "history = model.fit(dataset, epochs=500, verbose=0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iXBMO1HM9AHX",
        "colab_type": "code",
        "outputId": "108546d0-c2c6-492e-f159-fdcba7b9dc6e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "source": [
        "loss = history.history['loss']\n",
        "epochs = range(len(loss))\n",
        "plt.plot(epochs, loss, 'b', label='Training Loss')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xakiRU7R7WAo",
        "colab_type": "code",
        "outputId": "096efe05-c5bb-4447-f70b-f283aa0e2aea",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        }
      },
      "source": [
        "# Plot all but the first 10\n",
        "loss = history.history['loss']\n",
        "epochs = range(10, len(loss))\n",
        "plot_loss = loss[10:]\n",
        "print(plot_loss)\n",
        "plt.plot(epochs, plot_loss, 'b', label='Training Loss')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[53.09354117481979, 52.1831918736094, 51.47757688109408, 50.85410120266, 50.113298681593434, 49.520537466855394, 48.92888628969487, 48.29960509624678, 47.824282364992754, 47.364486267640416, 46.958202871342294, 46.53395357230275, 46.159338396603296, 45.80757097067292, 45.51912335071367, 45.245164867283144, 44.97992592644446, 44.73018188476563, 44.501735551578484, 44.263459134839245, 44.11395786128093, 43.87264674471826, 43.59897144356954, 43.4629123884378, 43.25828546347077, 43.10063603194718, 42.96753429137554, 42.82569573785841, 42.69157193370701, 42.51900374422368, 42.47962917839129, 42.32191896733549, 42.181012269639474, 42.067315359213914, 41.9578428995978, 41.83906704224262, 41.742973138868194, 41.64591475024666, 41.54783007828231, 41.438870608929506, 41.39809998974358, 41.273130176976785, 41.2456770080881, 41.11789184648966, 41.096483486214865, 40.98707706412089, 40.904327227405666, 40.84683983399696, 40.78635354189529, 40.709604176786755, 40.67530912418955, 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          "name": "stdout"
        },
        {
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          "data": {
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dPpRx2zZo2hQeeEDrxYgkiQJdPteggc82nTYNBg/2G6UlJbB/f9yViUh1KNDlKGbwzW/C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\nIpIS/x+0DkIShIV2cQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YUOPUeHWvvBG",
        "colab_type": "code",
        "outputId": "c39dcfe8-0ef8-47a3-cd8c-cda3fd13ab32",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        }
      },
      "source": [
        "forecast = []\n",
        "for time in range(len(series) - window_size):\n",
        "  forecast.append(model.predict(series[time:time + window_size][np.newaxis]))\n",
        "\n",
        "forecast = forecast[split_time-window_size:]\n",
        "results = np.array(forecast)[:, 0, 0]\n",
        "\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "\n",
        "plot_series(time_valid, x_valid)\n",
        "plot_series(time_valid, results)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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OGUBTOkFd0uDoYBaA/d4a7rv8s9CyDkY6I2FNb0KfHnXQGcgUQxEH5XYalWHN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pxFNgctCAgQIECAE0RAzvKAy4fchaSyKExxzFQk4vFvstjeCsBK/WV+EfomZ6ee67+TzffD799O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\nbZznqqwpIoeCPmllU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KC4vxmAUZ8zEamgqi7fZK1dB6wLbz8PnFrN8xERMcbQkoiyZyBJ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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W-GPjL2wv0yc",
        "colab_type": "code",
        "outputId": "0bc28f60-da65-4c6a-a9c0-7c371ff0c87c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "tf.keras.metrics.mean_absolute_error(x_valid, results).numpy()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4.925656"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    }
  ]
}